Saturday morning, I joined Dave Linthicum on his cloud computing podcast to discuss our impressions and findings from Cloud Connect.  Check out the podcast.  Learn what Kung Fu Panda is doing in the cloud.

Once again, the day opens with short keynotes. 

Starting us off is James Staten of Forrester.  James gives us two words to think about in respect to cloudonomics: Down & Off.  When the application (resource) isn’t in use, you can turn it off.  When you turn it off, you aren’t paying. 

James says to write applications in components that are as small as possible.  Only turn on what you need, when you need it.  James is describing cloud applications that have a zero footprint until a request is made.  [Sounds event-driven to me.  I like it.]

In the same respect, monitor performance thresholds to determine when an instance can be turned off.  Make “down” and “off” part of your design. 

[Of course, need to be smart in “systems management” with a hyper distributed, small component architecture.  Need to understand/monitor/manage the collective, to complete transactions].

Scott Baker, Director of Systems Engineering and Operations, Eventbrite

Scott is buzzing through a history of datacenter building and love.  However, he’s learned to love the cloud.  A favorite point of the crowd is “To have agile development, you need to have agile operations”.  The cloud, according to Scott, provides agile operations.

Oriol Vinyals, PhD Student, UC Berkeley, Microsoft Research Fellow: Building the Overmind: AI and Cloud Computing

Oriol is looking at artificial intelligence, Starcraft and the Cloud.  Starcraft, according to Oriol, is a strategy game.  To win, you need to gather resources, produce units and attack your opponent.  Starcraft, apparently, is a good AI problem to solve.

Challenges: long horizon, concurrent, partially observable (don’t see the complete board), and real-time.

Connections to cloud computing: resources, tasks, opponents & weapons (users & servers).

Oriol shows videos of swarms and defenders responding, you can see parallels to cloud computing and effective management of resources.

Marvin Wheeler, Chief Strategy Officer, Terremark, on the Open Data center Alliance.  From the website:

“The Open Data Center Alliance is an independent consortium comprised of leading global IT managers in a wide range of vertical segments, who have come together to provide a unified vision for long-term data center requirements.

In support of its mission, the Alliance is developing and delivering an Open Data Center Usage Model Roadmap, which defines Usage Model requirements to resolve key IT challenges and fulfill cloud infrastructure needs into the future. This vendor-agnostic roadmap serves as the foundation for member planning of future data center deployments, and relies on open, interoperable, industry-standard solutions.”

Marty Kagan, President and Co-Founder, Cedexis on Cloud Performance Data

Cloud Bakeoff: Amazon EC2, Google App Engine, Joyent, Rackspace and Windows Azure.  Bitcurrent is publishing a study.  EC2 East wins for HTTP request, but need to factor in request origin for best performance.

Neal Sample, Vice President, Architecture, Technology Product Management, Developer Program, eBay

Neal waled through models and curves on cloud bursting.  eBay has moved from 2000 servers down to 800 servers, with excess requests bursting to the public cloud.  By reducing infrastructure costs, eBay has been able to redirect that investment into business intelligence, providing additional value to the business.

Session Abstract: In many ways, Big Data is what clouds were made for. Computing problems that are beyond the grasp of a single computer—no matter how huge—are easy for elastic platforms to handle. In this session, big data processing pioneer Colin Clark will discuss how to discover hidden signals and new knowledge within in huge streams of realtime data, applying event processing design patterns to events in real time.

Speaker – Colin Clark, CTO, Cloud Event Processing

Colin opens talking about high velocity, big data.  Then, gives his Complex Event Processing Criteria:

1. Domain Specific Language
2. Continuous Query
3. Time/Length Windows
4. Pattern Matching

Example of what Colin is talking about: “Select * from everything where itsInteresting = toMe in last 10 minutes”

How much data does that return? How much processing will it take? 

Limitations of current CEP solutions: memory bound, compute bound and black box.  Using CEP, can analyze data in-flight, but have limitations. Other challenge is time series analysis.

A technique available for time series analysis is symbolic aggregate approximation (SAX). 

Colin is describing the construction of a “SAX word” from a days worth of IBM trading.  Then, search history for that same word, to find a pattern.

Getting closer to solving the high velocity, big data problem.  But, still too much data to process.  So, the next element in cloud event processing is Map/Reduce. 

Still though, need to address the real-time (event-driven) aspect.  Brings us to virtualized resources (cloud).

So, assuming I captured this correctly: High velocity, big data = CEP + SAX + Streaming Map/Reduce + virtualized resources, which equals Cloud Event Processing’s Darkstar.

Today, Darkstar is working on Wall Street, doing market surveillance at the exchange.  Speaking with Colin in the hallway, we discussed non-capital market prospects as well.

Speaker – Jinesh Varia, Technology Evangelist, Amazon Web Services

Jinesh is doing a nice job describing the evolution of a fictional (dating) website that is live only 3 hours a week.  In telling the story, he is walking through the site’s evolution, the developer’s knowledge of patterns (really good design practices) and the related AWS cloud offerings (patterns of use).

Pattern #1: Design for failure and nothing will fail

Pattern #2: Always edge cache your static data (Amazon CloudFront)

Pattern #3: Implement elasticity

Next challenge for the developer is dealing with scale.  Principles of elastic cloud architectures:

1. Resilient to reboot & re-launch: automatically re-launch and restart
2. stateless: extract stateful components and make them stateless
3. Packable into an AMI
4. Decouple: Isolate the components using Amazon SQS (queues). Decouple code with deployment and configuration.

Jinesh calls out the Netflix Chaos Monkey pattern. 

Elasticity also means scale-out.  So, the developer adds Elastic Load Balancer and create an Auto Scaling Group.

Pattern #4: Leverage Multiple Availability Zones (Feature on RDS, creates a standby slave)

Pattern #5: Isolate read and write traffic; Isolate static and dynamic traffic (Use RDS read replica)

Pattern #6: Automate your in-cloud Software Development & Deployment Lifecycle (Automate using cloud APIs – scripts)

Scripts provide: repeatability, savings (suspend/resume), productivity, and freedom to experiment.

Interesting point, used to “move” from staging to production.  With cloud, you can make Stage into Production, and then drop the current Production instances.  Saves time, reduces errors.

Pattern #7: Cache as much as possible (Memcache in new cache tier)

Pattern #8: Hardening security at every stage (infrastructure security, application security and services security)

Pattern #9: Go Global Quickly with a Single API

Pattern #10 Keep optimizing and see the savings in the next month’s bill (example, use reserve instances)

Started with single node architecture, now global, scaleable, fault tolerant application, while minimizing run-time cost.

Good session.

Cloudonomics: Private, Public, or Hybrid?

How should one quantify the ROI, costs, and benefits of cloud alternatives? What are the cost drivers of private, public, and hybrid delivery models, and how will they evolve over time? Is pay-per-use good or bad? What are the main cognitive biases in enterprise decision-making?

Speaker – Joe Weinman, Communications, Media, and Entertainment Industry Strategic Programs, HP

I’m starting the tracks with Joe Weinman’s cloudonomics session.  Joe’s session last year was a favorite of mine, so I’m back for more insights.

Joe is talking about some common cloud points/arguments that aren’t so clear.  A few comments:

• Economies of scale are the key to cloud benefits.  Designing building your own server, is not an economy of scale. Locating near rivers and cheap power is not an economy of scale, it’s an economy of location. 
• For larger companies, Capex vs. Opex is an accounting decision.  You can reserve cloud instances and then capitalize that expenditure.  Depends on what you need.
• The end-state is probably going to be a hybrid cloud, some owned, some on-demand.

How to quantify value?

1. unit cost reduction
2. total cost reduction
3. opportunity cost reduction
4. time & profitability improvement
5. revenue growth
6. customer experience enhancement
7. customer satisfaction / loyalty
8. risk reduction
9. competitive vitality
10. life or death – winner take all dynamics

See Joe’s Meteorology slide, captured & posted by Randy Bias.

Joe referenced his 10 laws of cloudonomics. I also liked his 10 laws of behavioral economics.

Vapor or Cloud: virtualize or defer. Virtualization won’t give you 100% utilization.  Defer, is deferring the workload.  Not possible in consumer, event-driven workloads (tax filing, holiday shopping, sports event).

Hybrid: “Own the base, rent the spike”.

All other things being equal, if cloud services cost less than enterprise IT, then, use them.

If cloud services cost more than enterprise IT, then, don’t jump to conclusions.  Need to consider demand spikes / patterns.  Best to see Joe’s papers that demonstrate the math, decision factors.

Some architecture options for Hybrid Clouds:

1. Pure Utility Cloud
2. Mixed-Rate Hosting/Cloud
3. Cloudbursting
4. Front-End / Back-end – leave back-end, move front-end to cloud

Caveat: Remember the data.  If it costs more to move (network) the data payload than the resource savings, don’t do it.

Next point: Time is money, the value of on-demand.  Paper (pdf) & Abtract:

"Cloud computing and related services offer resources and services “on demand.” Examples include access to “video on demand” via IPTV or over-the-top streaming; servers and storage allocated on demand in “infrastructure as a service;” or “software as a service” such as customer relationship management or sales force automation. Services delivered “on demand” certainly sound better than ones provided “after an interminable wait,” but how can we quantify the value of on-demand, and the scenarios in which it creates compelling value?

We show that the benefits of on-demand provisioning depend on the interplay of demand with forecasting, monitoring, and resource provisioning and de-provisioning processes and intervals, as well as likely asymmetries between excess capacity and unserved demand…"

Smooth Operator: the value of demand aggregation Paper (pdf) & abstract:

“In industries such as cloud computing, lodging, and car rental services, demand from multiple customers is aggregated and served out of a common pool of resources managed by an operator. This approach can drive economies of scale and learning curve effects, but such benefits are offset by providers‘ needs to recover SG&A and achieve a return on invested capital. Does aggregation create value or are customers‘ costs just swept under a provider‘s rug and then charged back?

Under many circumstances, service providers—which one might call "smooth" operators—can take advantage of statistical effects that reduce variability in aggregate demand, creating true value vs. fixed, partitioned resources serving that demand.”

I think I’ll download some of Joe’s papers for the flight home. 

The conference opens with short keynotes and a panel discussion on private clouds. 

First up, fittingly, is Werner Vogels, CTO of Amazon. Werner opens with a riff on “the map isn’t the territory” and “the model isn’t reality”.  From there, he goes to the 3-layer SaaS – PaaS – IaaS model.  That model, is just a model.  It shouldn’t restrict our view or understanding of cloud computing.  Everything as a (cloud) Service.  You should be able to use any service as if it is a cloud itself.  This is thinking behind Elastic Beanstalk.  “Let 1000 platforms bloom”. 

Via the cloud, enterprises and startups have access to the same software services.  Werner calls out security services and big data services.  Analytics is typically a domain of enterprise, but the cloud gives access to deep analytics to startups.

Werner’s pitch today, “the ecosystem is what defines the cloud”.  He is calling out some ecosystem examples.  It should be noted though, that the twitter back channel is mixed positive and negative.  There has been a running joke on twitter that “every time Jeff Barr writes a new post, a startup [AWS related service] dies”.  On the other hand, Amazon’s expanding offerings, enables many business and software startups.  So, it depends on your perspective.

Next, Dr. Lew Tucker, now with Cisco, on “The network is the computer, once again”.  Lew is working his way to the Internet of Things, but hasn’t called it that.  He sees “a world of many clouds”, media, government, industry etc.  But, the underlying technology model will be the same. This brings him to Cisco’s position on the new data center, insulate infrastructure and run it as a service. And you know, the network is king in this new model.

Now, Randy Bias of Cloud Scaling.  Randy is talking about enterprise cloud myths.  1. enterprises aren’t using Amazon 2. enterprises want to move legacy to the cloud, not develop greenfield apps.

What he is seeing, working on, is greenfield applications on public clouds for enterprise clients.  Enterprises aren’t moving legacy to the cloud.  This isn’t an outsourcing exercise.

Randy is walking through Amazon growth trends, “Amazon is winning”.  Randy’s takeaways: go commodity, serve greenfield, embrace the change.

Now, Alistair Croll is moderating a mini-panel on private clouds.  The panelists are:

• Matt Thompson, General Manager, Developer and Platform Evangelist, Microsoft
• Mathew Lodge, Sr. Director, Cloud Product Marketing, VMware

This is a contrast to Randy Bias’ talk.  Both panelists talking about porting existing applications to the cloud, and the need to port applications between public and private clouds.

Alistair: Data has surface tension, data wants to be together.  In respect to the cost of moving data around. 

In some cases, public cloud is front-end, data is in the back-end (private cloud).  – Matthew Lodge

Matt Thompson brings up public data sets, cloud is the best place to aggregate and process public data sets.  [Again, compute where the data lives]

Kevin McEntee, Vice President of Systems Engineering, Netflix

The Netflix story starts in 2008, with the highly publicized systems outage that delayed DVD shipments.  “Went to the cloud looking for high-availability, found agility for business and developers.  Agility by eliminating complexity”.

Kevin calls out work of Fred Brooks, No Silver Bullet.  Need to eliminate accidental complexity.  Netflix 2010, 80% of customer transactions running in the cloud.

In the cloud architecture, there is no single point of control over cloud spending.  Cloud enables running with this business culture, “responsible individuals, worthy of freedom”.

For more on Netflix, see this interview and the Netflix Technology blog.

Now up is Willy Chiu, Vice President, IBM Cloud Labs.  If you’ve followed the Jeopardy! Watson story, then you know what’s being said.  If not, here’s a link.

Todd Papaioannou, Vice President, Cloud Architecture, Yahoo.  This is an end-user story.  Building the Yahoo! cloud to support Yahoo! services.  Todd concentrates on elasticity. 

What are impediments for delivering truly elastic clouds?  Spin-up time is a major issue for ‘spike” events. 15-minutes to spin up a new VM is too long.  So, load shedding is the only current option for them. 

Derek Chan, Head of Digital Operations, Dreamworks Animation SKG

Derek is responsible for overall compute resources at Dreamworks.  Derek is an end-user of cloud computing, not building clouds. 

A single film, 4-5 years, 50+ million CPU hours.  Imagine having a dozen films in flight.  Tremendous peaks and troughs.  Paying for only what they need is tremendous benefit.

In 2003, used HP’s utility rendering service.  Wasn’t a cloud.  Was off-site resource usage.

Need to co-locate compute and data.

In 2010, released 3 CG features in one year.  Leveraged cloud to address the peaks.  Over 7 million compute hours sent to IaaS.

2011, more movies, more cloud.  Increase cloud capacity 10x.  Increasing network bandwidth 3x. 

Artists don’t know, don’t care about where rendering happens.

Cloud stack: RHEL (O/S), RHEV (virtualization), MRG (message queue), WebLogic, Jboss (middleware), deltaCloud (management)

Upcoming challenges: multi-tenancy, completely flex (payment model), cloud storage

Can see the evidence in Kung Fu Panda 2, 20% done in cloud.

Today and tomorrow I’ll be blogging from Cloud Connect 2011 in Santa Clara.  There are tracks on Cloud Economics, Security, Culture, Risk and Governance, Data & Storage, Design Patterns, DevOps, Performance and Monitoring, and (surprise) Private Cloud.  Last year, “private cloud” was more of a slur, than a strategy.  Nice to see the embrace of a common enterprise adoption strategy (or step path).

I’ll be attending a variety of sessions, taking some briefings and connecting with my cloud friends.  Follow my coverage here on elemental cloud computing and twitter.  The conference tag is #ccevent

This morning, I traded top cloud computing stories for January with David Linthicum and Bill Russell on the Cloud Computing Podcast.  We had one common story, giant hint on the left.  Check out our podcast.

The WSJ published an interesting article on commercial farming advances in Japan.  According to the article, “The aim is to bring the concepts of lean manufacturing and continual improvement, or kaizen, to farming.”

The farmers are employing sensors, analytics, real-time location information and cloud computing to optimize planting time, crop rotation, worker productivity and threat (infection) detection.

“…Now the head of a commercial farm in the southern Japanese prefecture of Miyazaki, Mr. Shinpuku is back manning a desk with his eyes glued to a Web browser tracking every movement of his workers who handle 60 different fruits and vegetables across its 100 hectares.

"I don’t want to do this. My eyes will get bad," said Mr. Shinpuku, the 58-year-old president of his commercial farm Shinpuku Seika, which is comprised of 300 different plots of land. "I put up with it, because the benefits are obvious. Without this computer, I can’t do my job."

Shinpuku Seika is among the first farms to implement a Web-based "cloud computing" service developed by Japanese technology firm Fujitsu Ltd. Cloud computing is a loosely defined business term in which companies rent computing power from remote data centers via the Internet instead of buying machines to run software in house.

Shinpuku Seika has placed sensors out in its fields to collect readings on temperature, soil and moisture levels. Fujitsu’s computers then crunch the data and recommend when to start planting or what crops may be well-suited to a specific field.

In the past, farmers would make those decisions based on experience, but Mr. Shinpuku says a data-driven approach prevents younger, less experienced staff from making mistakes that could cost the bottom line.”

The pay-off?  Measured in cabbage of course:

“The system is already paying off for Shinpuku Seika, which generates about 1.5 billion yen ($18 million) in annual revenue. Last year, it doubled the size of its carrot harvest and raised its cabbage output by 12%.”

Read the full article.

Back in April 2009, McKinsey set the cloud computing community afire with a presentation arguing that corporate cloud computing adopters might expend more money using cloud versus traditional data center resources.  As reported by Steve Lohr in the NYTimes Bits blog:

“The McKinsey study, “Clearing the Air on Cloud Computing,” concludes that outsourcing a typical corporate data center to a cloud service would more than double the cost.”

Many in the cloud computing space, including Gartner’s highly respected Lydia Leong, immediately took the ‘math’ behind this report to task.

My issue at the time wasn’t the math, but the premise.  I couldn’t imagine any CIO – in their right mind – outsourcing their entire enterprise datacenter to the cloud.

To cloud or not to cloud, should be evaluated and employed in the context of specific business needs, accounting for fit, risk, cost, opportunity and overall value.  This is no different from other technology investments. 

Fast forward to late 2010, McKinsey published a couple of reports highlighting the value and role of cloud computing in flexible and responsive organizations.  Different lens, different result.

In November, McKinsey published the results of their 5th business technology survey.  The headline is that executives want more immediate value from IT as well as forward looking strategies that support growth and innovation.

“The demands on IT, results show, are more intense than ever. While many organizations express basic satisfaction with their own IT departments, new hurdles face IT executives as business units are demanding more value from the function. C-suite leaders are pressing IT executives for gains from transformational technologies like cloud computing, and they want IT to help turn growing stores of corporate data into information assets that support growth and guide innovation.”

Specific to cloud computing, 70% of the non-IT executives surveyed (n = 252) view cloud computing (any network delivered resource, including SaaS) as a way to increase business flexibility.  These executives also see IT and business continuity value, but not to the same degree as business flexibility.  I take this as a good sign.  Business executives “get” the true potential of cloud computing.

In December, McKinsey published an interesting paper entitled Reshaping IT management for turbulent times, which advocates “A new model for managing IT combines factory-style productivity to keep costs down with a more nimble, innovation-focused approach to adapt to rapid change.”

“In most organizations, IT began as a support function, leading to a one-dimensional management approach. However, technology-enabled products, interactive communications, and an “always on” information environment have thrust IT to the forefront, with critical implications for business growth and customer engagement. In addition, established practices, such as lean-management techniques, have highlighted the value of IT in reducing waste and increasing productivity.

This deeper recognition of IT’s potential has given rise to a new management model consisting of two categories: “Factory IT” and “Enabling IT.” Factory IT encompasses the bulk of an organization’s IT activities, applying lessons from the production floor—scale, standardization, and simplification—to drive efficiency, optimize delivery, and lower unit costs. Enabling IT is focused on helping organizations respond more effectively to changing business needs and gain a competitive advantage by spurring innovation and growth.”

According to the report, there are 3 key components of the Factory Model:

1. Industrial IT – applying traditional business-management techniques to IT (such as Lean, disciplined governance, performance measurement, transparency)
2. Flexible IT factories – building IT that’s more responsive to changing business conditions, utilizing the cloud and agile development techniques:
   

   “The cloud. Cloud computing offers access to information, processing, and storage through the network or an external service provider. This mode of delivery allows companies to purchase computer processing as a service, rather than making up-front investments in IT capacity and in-house support staff. The New York Times, for example, digitized and catalogued more than 100 years of archived articles for its Web site in a 24-hour period by using Amazon.com’s cloud
   offering, avoiding the need to configure and operate a set of servers for a onetime effort.

   …Together, the cloud and agility can make the IT factory more nimble, with lower costs and faster delivery.”

3. Holistic business cases – cutting complexity through improved planning (complexity builds over time, result of systems evolving beyond initial intent)

While not stated in the report, I believe cloud computing supports the Rapid Experimentation component of the Enabling IT Model:

“Where lean manufacturing and Factory IT seek to avoid errors, Enabling IT’s mind-set tolerates (and even encourages) the mistakes that result from experimentation and iteration as long as they happen quickly, the outcomes are measured, and the lessons are incorporated into the team’s thinking. More companies are embracing rapid experimentation as a way to develop, refine, and upgrade their services or products.

Capital One and Google, for example, have been at the forefront of this trend with their credit cards and online services, respectively. That wave is spreading to traditional players: P&G’s Vocalpoint, a network of mothers, provides feedback on new product ideas. Similarly, a leading fast-food company is using IT systems and analytics at test sites to gauge the impact of new menu choices on store-level revenue, operations, and customer experience.

Such experimentation requires the right set of technical capabilities and a flexible IT environment. Managers must employ tools to define, build, test, and improve new products quickly, integrating feedback from both internal stakeholders and a set of users or customers.

Responsive IT support is a vital component of this effort. By assembling a team to work hand in hand with the managers on these new business offerings, IT provides essential support to help build and modify business processes and systems rapidly.”

Changing their lens from IT expense to business value, McKinsey has identified several (viable) value propositions for cloud computing.  Switch your own lens.  What do you see?